arXiv subjects

Enrique Areyan Viqueira

Publications and source records attributed to Enrique Areyan Viqueira.

8 recordsLinked to original sources

arXiv2026

Zero Collapse: A Failure Mode of Policy Gradient Methods in Discontinuous Reward Environments

Policy-gradient learning can deteriorate even after substantial initial improvement. We study this behavior in a repeated first-price auction setting with a sharp threshold between winning and losing bids. We identify a failure mode, zero collapse, in which a policy shifts into persistently near-zero reward as informative outcomes become rarely sampled. Experiments with REINFORCE and a TD state-value actor-critic illustrate this failure and evaluate practical mitigations. Adaptive update-magnitude control and smooth-output parameterization support sustained REINFORCE learning, while a learned baseline substantially stabilizes the fixed-rate comparison. Some actor-critic runs still collapse under these interventions. A diagnostic near one observed collapse reveals severe continuation-value errors that reverse the relative desirability of actions across the winning threshold. These results illustrate how thresholded outcomes, policy updates, and inaccurate value estimates can contribute to zero collapse, and demonstrate practical mitigations in the studied setting.

cs.LG↗

arXiv2025

Automating Credit Card Limit Adjustments Using Machine Learning

Venezuelan banks have historically made credit card limit adjustment decisions manually through committees. However, since the number of credit card holders in Venezuela is expected to increase in the upcoming months due to economic improvements, manual decisions are starting to become unfeasible. In this project, a machine learning model that uses cost-sensitive learning is proposed to automate the task of handing out credit card limit increases. To accomplish this, several neural network and XGBoost models are trained and compared, leveraging Venezolano de Credito's data and using grid search with 10-fold cross-validation. The proposed model is ultimately chosen due to its superior balance of accuracy, cost-effectiveness, and interpretability. The model's performance is evaluated against the committee's decisions using Cohen's kappa coefficient, showing an almost perfect agreement.

cs.LG↗

arXiv2022

Computational and Data Requirements for Learning Generic Properties of Simulation-Based Games

Empirical game-theoretic analysis (EGTA) is primarily focused on learning the equilibria of simulation-based games. Recent approaches have tackled this problem by learning a uniform approximation of the game's utilities, and then applying precision-recall theorems: i.e., all equilibria of the true game are approximate equilibria in the estimated game, and vice-versa. In this work, we generalize this approach to all game properties that are well behaved (i.e., Lipschitz continuous in utilities), including regret (which defines Nash and correlated equilibria), adversarial values, and power-mean and Gini social welfare. Further, we introduce a novel algorithm -- progressive sampling with pruning (PSP) -- for learning a uniform approximation and thus any well-behaved property of a game, which prunes strategy profiles once the corresponding players' utilities are well-estimated, and we analyze its data and query complexities in terms of the a priori unknown utility variances. We experiment with our algorithm extensively, showing that 1) the number of queries that PSP saves is highly sensitive to the utility variance distribution, and 2) PSP consistently outperforms theoretical upper bounds, achieving significantly lower query complexities than natural baselines. We conclude with experiments that uncover some of the remaining difficulties with learning properties of simulation-based games, in spite of recent advances in statistical EGTA methodology, including those developed herein.

cs.GT↗

arXiv2022

A Consumer-Theoretic Characterization of Fisher Market Equilibria

In this paper, we bring consumer theory to bear in the analysis of Fisher markets whose buyers have arbitrary continuous, concave, homogeneous (CCH) utility functions representing locally non-satiated preferences. The main tools we use are the dual concepts of expenditure minimization and indirect utility maximization. First, we use expenditure functions to construct a new convex program whose dual, like the dual of the Eisenberg-Gale program, characterizes the equilibrium prices of CCH Fisher markets. We then prove that the subdifferential of the dual of our convex program is equal to the negative excess demand in the associated market, which makes generalized gradient descent equivalent to computing equilibrium prices via tâtonnement. Finally, we run a series of experiments which suggest that tâtonnement may converge at a rate of $O\left(\frac{(1+E)}{t^2}\right)$ in CCH Fisher markets that comprise buyers with elasticity of demand bounded by $E$. Our novel characterization of equilibrium prices may provide a path to proving the convergence of tâtonnement in Fisher markets beyond those in which buyers utilities exhibit constant elasticity of substitution.

cs.GT↗

arXiv2021

Integrated Vehicle Routing and Monte Carlo Scheduling Approach for the Home Service Assignment, Routing, and Scheduling Problem

We formulate and solve the H-SARA Problem, a Vehicle Routing and Appointment Scheduling Problem motivated by home services management. We assume that travel times, service durations, and customer cancellations are stochastic. We use a two-stage process that first generates teams and routes using a VRP Solver with optional extensions and then uses an MC Scheduler that determines expected arrival times by teams at customers. We further introduce two different models of cancellation and their associated impacts on routing and scheduling. Finally, we introduce the Route Fracture Metaheuristic that iteratively improves an H-SARA solution by replacing the worst-performing teams. We present insights into the problem and a series of numerical experiments that illustrate properties of the optimal routing, scheduling, and the impact of the Route Fracture Metaheuristic for both models of cancellation.

cs.AI↗

arXiv2021

Learning Competitive Equilibria in Noisy Combinatorial Markets

We present a methodology to robustly estimate the competitive equilibria (CE) of combinatorial markets under the assumption that buyers do not know their precise valuations for bundles of goods, but instead can only provide noisy estimates. We first show tight lower- and upper-bounds on the buyers' utility loss, and hence the set of CE, given a uniform approximation of one market by another. We then develop a learning framework for our setup, and present two probably-approximately-correct algorithms for learning CE, i.e., producing uniform approximations that preserve CE, with finite-sample guarantees. The first is a baseline that uses Hoeffding's inequality to produce a uniform approximation of buyers' valuations with high probability. The second leverages a connection between the first welfare theorem of economics and uniform approximations to adaptively prune value queries when it determines that they are provably not part of a CE. We experiment with our algorithms and find that the pruning algorithm achieves better estimates than the baseline with far fewer samples.

cs.GT↗

arXiv2019

Learning Equilibria of Simulation-Based Games

We tackle a fundamental problem in empirical game-theoretic analysis (EGTA), that of learning equilibria of simulation-based games. Such games cannot be described in analytical form; instead, a black-box simulator can be queried to obtain noisy samples of utilities. Our approach to EGTA is in the spirit of probably approximately correct learning. We design algorithms that learn so-called empirical games, which uniformly approximate the utilities of simulation-based games with finite-sample guarantees. These algorithms can be instantiated with various concentration inequalities. Building on earlier work, we first apply Hoeffding's bound, but as the size of the game grows, this bound eventually becomes statistically intractable; hence, we also use the Rademacher complexity. Our main results state: with high probability, all equilibria of the simulation-based game are approximate equilibria in the empirical game (perfect recall); and conversely, all approximate equilibria in the empirical game are approximate equilibria in the simulation-based game (approximately perfect precision). We evaluate our algorithms on several synthetic games, showing that they make frugal use of data, produce accurate estimates more often than the theory predicts, and are robust to different forms of noise.

cs.GT↗

arXiv2017

On Approximate Welfare- and Revenue-Maximizing Equilibria for Size-Interchangeable Bidders

In a Walrasian equilibrium (WE), all bidders are envy-free (EF), meaning that their allocation maximizes their utility; and the market clears (MC), meaning that the price of unallocated goods is zero. EF is desirable to ensure the long-term viability of the market. MC ensures that demand meets supply. Any allocation that is part of a WE is also welfare-maximizing; however, it need not be revenue-maximizing. Furthermore, WE need not exist, e.g., in markets where bidders have combinatorial valuations. The traditional approach to simultaneously addressing both existence and low revenue is to relax the MC condition and instead require the price of unallocated goods be some, positive reserve price. The resulting solution concept, known as Envy-Free Pricing (EFP), has been studied in some special cases, e.g., single-minded bidders. In this paper, we go one step further; we relax EF as well as MC. We propose a relaxation of the EF condition where only winners are envy-free, and further relax the MC condition so that unallocated goods are priced at least at the reserve. We call this new solution concept Restricted Envy-Free Pricing (REFP). We investigate what REFP entails for single-minded bidders, and show that for size-interchangeable bidders (a generalization of single-minded introduced in this paper) we can compute a REFP in polynomial time, given a fixed allocation. As in the study of EFP, we remain interested in maximizing seller revenue. Instead of computing an outcome that simultaneously yields an allocation and corresponding prices, one could first solve for an allocation that respects a reserve price, and then solve for a corresponding set of supporting prices, each one at least the reserve. This two-step process fails in the case of EFP since, given a fixed allocation, envy-free prices need not exist. However, restricted envy-free prices always exist...

cs.GT↗